Heterogeneous multitask metric learning across multiple domains
Distance metric learning plays a crucial role in diverse machine learning algorithms and applications. When the labeled information in a target domain is limited, transfer metric learning (TML) helps to learn the metric by leveraging the sufficient information from other related domains. Multitask m...
Main Authors: | Luo, Yong, Wen, Yonggang, Tao, Dacheng |
---|---|
Other Authors: | School of Computer Science and Engineering |
Format: | Journal Article |
Language: | English |
Published: |
2020
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/139878 |
Similar Items
-
Transferring knowledge fragments for learning distance metric from a heterogeneous domain
by: Luo, Yong, et al.
Published: (2020) -
Optimized Laplacian SVM With Distance Metric Learning for Hyperspectral Image Classification
by: Yanfeng Gu, et al.
Published: (2013-01-01) -
Stamping Tool Conditions Diagnosis: A Deep Metric Learning Approach
by: Zaky Dzulfikri, et al.
Published: (2021-07-01) -
Recent Developments in Digital Mathematics Libraries
by: Jiří Rákosník, et al.
Published: (2014-09-01) -
European Digital Mathematics Library EuDML. Current State and Future Plans
by: Jiří Rákosník, et al.
Published: (2016-09-01)